Abstract
Forecasting domestic credit growth based on ARIMA model: Evidence from Vietnam and China
Highlights
Autoregressive integrated moving average (ARIMA) Method is applied by many researchers to analyze and predict time series (Hodrick & Prescott, 1997)
The results showed that autoregressive integrated moving average (ARIMA) ((12), 1, 0) is a model best suited to time series data of consumer price index (CPI) and forecast CPI and subsequently the inflation rate, (Jere & Mubita, 2016; Kishwer, et al.,2014)
The paper is based on this model to estimate the credit / GDP ratio of time series (1996-2017) and the results show the optimal forecast model with p = 2, q = 4 and d = 3
Summary
Autoregressive integrated moving average (ARIMA) Method is applied by many researchers to analyze and predict time series (Hodrick & Prescott, 1997). This model was studied by George Box and Gwilym Jenkins in 1976. Previous studies used this method to analyze and forecast factors such as Inflation Rate, Long-Run Neutrality of Money in a Developing Country, GDP Series of Pakistan, and forecasting economic growth etc. The authors applied the Autoregressive Integrated Moving Average (ARIMA) model to forecast Zambia's inflation rate by using the monthly consumer price index (CPI) data from 2010 to 2014. Financial services work through efficient fund resource mobilization and credit expansion is to raise the level of investment and efficient capital accumulation (Seher Nur 2011; Sreerama et al, 2012)
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